Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians

Abstract

Background Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world’s largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS). Objective We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations. Methods and analysis We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the “100 Million Brazilian Cohort”, over a 19-year period (2000–2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios. Conclusion The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.

Mechanistic modeling of social conditions into disease predictions for public health intervention-analyses: application to HIV

“…Statistical models, such as regression, directly fit a model between health outcomes (HIV) as response variable and all other metrics including social conditions as independent variables [26], and thus, do not consider the behavioral and disease dynamics. Mechanistic models simulate behaviors, such as sexual behaviors, e.g., number of partners, and condom-use, and care behaviors, e.g., HIVtesting, care retention and treatment adherence, to generate the dynamics of transmission and disease progression [27]- [30].…”

“…Also, data on associations between behaviors and social conditions are mostly univariate, e.g., correlation between condom-use and housing stability or between number of partners and housing stability, but the multivariate associations, e.g., between condom-use, number of partners, housing stability, and poverty, are not available for all populations. Current models in the literature either provide a general framework that assume parameters are independent or that data are available [25], [27], or have mostly focused on specific populations where individual-level data are available for extraction of the joint distributions, e.g., through randomized control trials that focus on selected individual-level metrics [28], individual-level surveys of a specific population [29], and large longitudinal (~18 years) individual-level datasets [30]. On one hand, it may be infeasible or expensive to generate such data for all populations, and on the other hand, using one nationally representative dataset would ignore the heterogeneity and thus disparities across populations.…”